Paper Title:
Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network
  Abstract

In the technology of motor fault diagnosis, current monitoring methods have become a new trend in motor fault diagnosis. This paper presents a motor fault diagnosis method based on Park vector and wavelet neural network. This method uses the stator current as the object of study. Firstly, it uses Park vector to deal with the stator current and filter out fundamental frequency component, thus the characteristics component of motor broken-bar will be separated from fundamental frequency component; Secondly, it uses five layers wavelet packet decomposition to pick up fault characteristic signal; Finally, we distinguish the fault by BP neural network, and use the simulation software of MATLAB to realize it. The test results show that: This method can detect the existence of motor broken-bar fault, and has a good value in engineering.

  Info
Periodical
Chapter
Chapter 2: Design of Machine, Mechanics and and Dynamic System
Edited by
Helen Zhang and David Jin
Pages
163-166
DOI
10.4028/www.scientific.net/AMR.382.163
Citation
Q. X. Zhang, J. Li, H. B. Li, C. Liu, "Motor Broken-Bar Fault Diagnosis Based on Park Vector and Wavelet Neural Network", Advanced Materials Research, Vol. 382, pp. 163-166, 2012
Online since
November 2011
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Price
$32.00
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